控制理论(社会学)
反推
李雅普诺夫函数
人工神经网络
计算机科学
有界函数
主动悬架
约束(计算机辅助设计)
悬挂(拓扑)
自适应控制
数学
执行机构
控制(管理)
非线性系统
人工智能
量子力学
几何学
物理
数学分析
纯数学
同伦
作者
Lei Liu,Changqi Zhu,Yan‐Jun Liu,Rui Wang,Shaocheng Tong
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2022-01-11
卷期号:34 (10): 7089-7098
被引量:22
标识
DOI:10.1109/tnnls.2021.3137883
摘要
A robust adaptive control method for a certain type of quarter active suspension system (ASS) is proposed in this work. The constraint issue of ASS is put into consideration primarily. Due to the limitation of the traditional barrier Lyapunov functions (BLFs), the integral barrier Lyapunov function (iBLF) is introduced to exert direct constraints on state variables in each stage under the backstepping frame, and neural networks (NNs) are applied to identify those unknown functions. Then, an adaptive law based on the projection operator is defined to eliminate the influence caused by the actuator failure. It is widely known that only the vertical displacement and velocity constraints are not violated, can the ASSs become stable and secure. It can be ultimately confirmed that all signals in the closed-loop system are bounded, and the control goals are satisfied. Last but not least, the feasibility of the approach is illustrated directly through a contrast simulation example.
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